Instructions to use mertkayacs/Tholos-2B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use mertkayacs/Tholos-2B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mertkayacs/Tholos-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mertkayacs/Tholos-2B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mertkayacs/Tholos-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mertkayacs/Tholos-2B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf mertkayacs/Tholos-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mertkayacs/Tholos-2B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf mertkayacs/Tholos-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mertkayacs/Tholos-2B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mertkayacs/Tholos-2B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use mertkayacs/Tholos-2B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mertkayacs/Tholos-2B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mertkayacs/Tholos-2B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mertkayacs/Tholos-2B-GGUF:Q4_K_M

Ollama
How to use mertkayacs/Tholos-2B-GGUF with Ollama:
```
ollama run hf.co/mertkayacs/Tholos-2B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use mertkayacs/Tholos-2B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mertkayacs/Tholos-2B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mertkayacs/Tholos-2B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use mertkayacs/Tholos-2B-GGUF with Docker Model Runner:
```
docker model run hf.co/mertkayacs/Tholos-2B-GGUF:Q4_K_M
```

Lemonade

How to use mertkayacs/Tholos-2B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mertkayacs/Tholos-2B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Tholos-2B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use mertkayacs/Tholos-2B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mertkayacs/Tholos-2B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default mertkayacs/Tholos-2B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mertkayacs/Tholos-2B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mertkayacs/Tholos-2B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "mertkayacs/Tholos-2B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Tholos-2B-GGUF

A 2B agent model built by senior AI engineer Mert Kaya: it passes 137 of 160 Tholos-Bench scenarios on one Kaggle T4 GPU (Q4_K_M, llama.cpp with a JSON schema), 25 more than the model it was trained from (evaluation).

GGUF builds of Tholos-2B, MiniCPM5-2B fine-tuned to be the agent in Tholos. The main card has the step format, the training data and the benchmark results. This page has the files and the commands.

File	Quantization	Size	sha256
Tholos-2B-Q4_K_M.gguf	Q4_K_M	1.56 GB	`65f4700e0e107d52f5a80c3c513b3387e8cfcef9cb7545f0a2c0a2293d1eadc3`
Tholos-2B-Q8_0.gguf	Q8_0	2.68 GB	`5e57b416fb515260276413a9fadc3a67ef3d81f6fceb427cefe475850df1c476`

The repo's SHA256SUMS file lists the same sums. Q4_K_M is the file our benchmark runs use.

llama.cpp

llama-server -hf mertkayacs/Tholos-2B-GGUF:Q4_K_M --jinja -c 16384 -t 4 -a tholos-2b \
  --host 127.0.0.1 --port 8080

Swap Q4_K_M for Q8_0 to load the larger file. Send a json_schema response format with each request so the server constrains decoding; the main card has a complete request.

Ollama

ollama pull hf.co/mertkayacs/Tholos-2B-GGUF:Q4_K_M

The repo carries a template and a params file. The template renders prompts the way the model was trained, with an empty think block before each answer, and matches the training render byte for byte. The params file sets the stop tokens and a 16,384-token context (Ollama's default is 4,096).

Without a template file Ollama picks one automatically. On the base model's official Q4_K_M file that choice left out the empty think block, and the file passed 48 of 160 Tholos-Bench scenarios with it and 96 of 160 with our template.

In Tholos, press Detect in Settings and add the model. Tholos asks Ollama for JSON mode and sends reasoning_effort: "none" on its own.